This study will develop tools and methods for 'reverse engineering' the transcription regulatory network from large-scale gene expression data. We will focus on two issues: how to identify modules of co-expressed genes and how to extract regulatory interactions between genes. Based on the results, a theoretical framework for studying the transcriptional network will be proposed, and 'design principles' underlying its function and structure will be formulated. The study will focus on the yeast S. cerevisiae as a model system and will utilize computer analysis of publicly available data, design and analysis of kinetic microarray experiments, and theoretical modeling. Specifically, we attempt to establish a novel '2D-signature' algorithm for identifying overlapping regulatory modules, and compare its performance with existing methods particularly the clustering approach. This algorithm provides a two-dimensional representation of a regulatory module, specifying both its gene content and the conditions where these genes are coordinately regulated. Application of this algorithm will include identifying the set of genes regulated by a given transcription factor, identifying novel regulatory motifs, and functional assignment. Theoretical predictions will be validated experimentally. The utility of the modular approach for recognizing high-level structure of the transcription network will be explored. Kinetic microarray experiments that highlight specific features of the transcriptional network will be designed, performed and analyzed. Methods for extracting regulatory relationships from these experiments will be developed.